Today, I’m visualizing marriage equality for everyone. In the U.S., the Supreme Court is hearing two landmark cases on gay marriage. Hoping the long journey is nearing a happy and just conclusion.
On a weekly basis (if I’m lucky) one of the things that I find myself most in need of is a common area to find real-life examples of the best practices that we all try to follow. But talk is cheap and a little bit of visual awesomeness goes a long way so…
Why? Well, I don’t know how many of you often find yourselves swimming upstream and in the dark when it comes to sweet-talking clients out of ideas that you know are, em, well, sometimes just a wee bit unusual, not realistic, not good practice, a few branches short of a tree etc., etc.. If you are, then you also know how, though these conversations can sometimes be rewarding, oftentimes they are not (all recipients of puzzled looks or polite silence followed by the inevitable request to “do it anyway” or “can’t you just…” raise your hands).
I’m hoping that this new platform will give us quick access to quality examples of information design–solutions that illustrate a specific direction or idea that we’re trying to pitch to our teams, stakeholders and clients. Often I find myself scrambling to create comps to better prove or show a point. Nothing wrong with that, but if there’s a place where I can follow knowledgeable designers and their work rather than wading through Google searches or sites that warehouse images, I’m all for it (though where would I be without my favorite beer graphic?).
The Visual.ly social media platform, coupled with the excellent blogs out there (ranging from good critiques on the visual.ly blog, to case studies and reality checks by chartsnthings, as well as the usual suspects like the Guardian and Flowing Data and many more) is a damn good thing, and I’m excited to see this take off.
If we use this tool wisely and well, does that mean no more animated 3D piecharts?
For the cyclists out there, I hope you’ll agree that a post-ride banana is about as life-affirming as a cold beer. For me, even in the dog days of a Washington, D.C. summer, a banana is the perfect, portable pick-me-up. So, imagine my delight when a friend sent me a six-way Venn banana diagram, in the most recent issue of the science journal Nature, showing the distribution of gene families in this most humble of fruits. I had to reach waaay back to biology class (and Wikipedia) to recall that monocots are one of two types of flowering plants (distinguished by having only one seed-leaf, for those of you dying to know). For the Venn geeks, the diagram actually uses A. W. F. Edwards’ six-set Venn diagram.
And if you like Venn diagrams more than bananas, here is one of my favorites, by Colin Harman. In math, Venn diagrams show relationships within sets. In real life, they allow cheeky designers to provide clients with a reality check.
And if you’d like to see how NOT to use a Venn diagram, FlowingData recently posted on a Mitt Romney graphic.
How timely. Last week I wrote about choosing the right chart. Juice Analytics recently created an interactive Chart Chooser, based on Andrew Abela’s original Chart Chooser decision chart (via FlowingData). Both tools are excellent and offer a great start to choosing the right chart/graph format for data. The interactive chart offers little in terms of best practices (it wasn’t designed to do that) but helpfully separates out different chart types by the data that you have (quantity, comparison, distribution, etc.). And the best part of the interactive is that it provides you with downloadable templates for both Excel and Powerpoint. I’ll try this and might write about how well it works for me in a business setting.
I actually like Andrew’s original (static) chart a little better, as I find the flow diagram does a nicer job of providing context for the decision-making process.
Put both of these things together and you’re off to a good start.
[UPDATE]: Read Naomi Robbin’s (Forbes) excellent counterpoint to the chart-by-menu mentality.
Ah, the glamorous life of the data visualization designer… to draw or not to draw? To obfuscate or not to obfuscate? I’ve been doing some reading lately about a debate that is making its way amongst the data viz community. At what point does too much illustration, creativity or innovation get in the way of the primary purpose of data visualization? And how well is the design community being transparent about art based on data versus data visualization? Or, to put it more simply, should data visualization be easy to understand and what happens when it’s not?
Allow me, first, to offer up my own definition, artfully cadged from people much smarter than I and enhanced by my own experience in the field, such as it is. So, data visualization is what, exactly?
Information served up visually in order to inform and improve/enhance our understanding of the data.
Clumsy, but I’m hitting the main points: inform and understanding. If pressed, I would add the word “easily.” Actually, it’s the word “easily” that prompted me to write this.
If you can’t understand a data visualization piece, then it’s pretty useless, isn’t it? Maybe it’s beautiful, but if you walk away more confused than you began, it’s useless. And if you walk away as confused, or a bit less confused, it’s still useless.
How far can we take this concept? Here is a quick survey of what folks have been saying lately. Props to infosthetics for providing a good starting point for these discussions. And here they are:
Stephen Few’s blog post on the two types of data viz is a good start. According to Few (Tufte’s alter-ego), there are two approaches to presenting data graphically—data visualization and data art. As he puts it, “rarely do the twain meet.” Therein lies the problem. They do meet. All the time. Though Few makes a good point—failing to distinguish between them creates confusion and harm, I would argue that the two are not mutually exclusive.
Few defines data visualizations as products created to inform, and “data art” as visualizations of data created to entertain—“art based on data”—something which can be judged accordingly.
My response? Would that the public were quite as discerning as he. The train has left the station and what we have before us is—at worst—a proliferation of eager designers too quick on the draw to consider the very important questions that need to be asked about the data that are being depicted. At best, a cadre of informed (and willing to learn) designers who humbly allow the information, the audience and the goals of the visualization to drive the design—who are loathe to add one extra pixel that doesn’t belong, and willing to take away any element that obscures a better understanding of the data. I’d like to think that I fall into the latter category but I fall somewhere in the middle, as do most designers.
Rather than drawing a bright line between these two approaches and dogmatically refusing to accept a middle ground, I suggest we embrace a blend of these when they are produced well—when they inform and present a clearer understanding of the data and are at the same time aesthetically pleasing. As a designer who chooses to serve both masters—art and data, I find joy in being able to translate a jumble of Excel rows and columns into a plain bar chart—sometimes the beauty lies in the hard work of sifting through the data and simplifying complexity. And sometimes the joy comes from experimenting with different formats and adding visual accents to enhance the data—provided, of course, that the user’s ability to understand the data is not impeded, but enhanced.
Nevertheless, I agree with Few’s depiction of the pitfalls of “data art” being misperceived as data visualization, and I’ll add one myself. In addition to spreading poor practice instead of best practice, it creates unrealistic expectations about what is acceptable in a data visualization, particularly for those of us who are working in the industry in a supportive capacity to researchers and writers with an uneven understanding of best practices (how many of us have been asked to create 3D graphics or exploding pie-charts on a whim?).
And a rising tide floats all boats. In this case, I’ll agree with Few’s point that the proliferation of “data art” and other fancy-schmanzy graphics that pass for data visualization imply that data viz is a closely-guarded secret known only to denizens of the data underworld (paraphrasing liberally from Mr. Few, here). But I take issue with his assertion that this prevents the “democratization of data”—implying that the public is somehow being dissuaded from engaging and creating data. For better or for worse, they aren’t. Just google “infographics.”
As an interesting aside, note that Eagereyes’ Robert Kosara wrote a primer on the two types of data visualization that Few discusses, waaaay back in 2007. Like Few, Kosara was also bothered by the blurred line between data and art. What Few calls “data art” Kosara called “artistic visualization.” Nonetheless, they each underscore the same points—keep data and art separate in order to be as transparent and clear about the data as possible. I agree with the goal.
As Kosara puts it, “looking at one type of visualization expecting the other will lead to disappointment and misunderstandings.”
Kosara, uses what is, in my opinion, one of the best data viz sites out there (infosthetics) as an example of sites that don’t make those distinctions, thus creating confusion. Granted, this was back in 2007. I wonder what he’d say now? Nonetheless, I disagree. Let’s not confuse lack of best practice (for example, normalizing your data to prove a point, and not being transparent about it) with the so-called sin of creating a piece that is visually striking. A designer can produce a graph with no artistic aspirations whatsoever that nonetheless obscures the data. And a designer can produce a terrific visual that observes best practices (to inform) and serves up the data artistically and well.
Adam Crymble has a different moniker for Few’s “data art” and Kosara’s “artistic visualizations.” He calls these graphics “shock and awe.” I love that term. Of all the discussions that I have read, Adam’s make the most sense to me. He doesn’t touch on all data viz that is artistic, but rather focuses on the extreme—and in this I strongly agree with the points he makes.
Adam Crymble: “shock and awe” graphics
We’ve all seen these very beautiful, complex visualizations that belong inside of a picture frame or a screensaver. Or, for a few seconds, they give us pause and food for thought.
I’ve seen them, written about them and admire them for what they are—unique explorations of the complexity of data. An artistic or visual expression of the complexity of the information we spew out and take in. But they don’t inform in the traditional sense of the definitions of data viz. They may underscore a pattern, convey a sense of weight through sheer numbers or complexity (as the example above does), but that’s about it. They’re pretty much impossible to understand on a granular level without some work.
Adam’s assertion that these complex visualizations have no place in the academic world is beyond my ken. For the record, the example above is mine, not his (see his post for his own, more humorous example). But if he is correct that peer reviewers are afraid to betray their lack of understanding of these graphics, and thus—through tacit acceptance—are endorsing their validity, well then that should concern all of us.
The most interesting point to be gleaned from Adam’s perspective, I think, is the bullying nature of shoving a terabyte of data in front of someone’s face and saying “Aren’t I clever? Don’t you get it?” I don’t. Point well-taken, Adam.
Mark Ravina writes an interesting rebuttal to Adam’s criticism of “shock and awe” graphics. He compares these artistic and complex visualizations to early feminist scholarship that provoked anger when it challenged the systemic sexism of the ivory tower. I’m a huge fan of confrontation and anger-provoking methods to push movements forward. In the early 90s, ACT-UP did the same thing for GLBT rights, if you’ll recall. Without ACT-UP, Queer Nation and Lesbian Avengers, there would be no fancy Human Rights Campaign fundraising dinners today. I get it.
But Ravina’s assertion that these complex visualizations of data somehow push the field forward is a bit much for me. He calls them “intellectual challenges.” I’m not so sure about that. How many of us who are willing to spend more than a few seconds trying to piece together a gazillion threads and data points in a fancy graphic. I think we consider it more of a waste of time to do anything other than admire the concept, the novelty of the presentation and then move on. Intellect doesn’t play a big role here (the creator, on the other hand, gets some bragging rights for creativity). Does it stick? Does it move the field forward? Um, maybe, sometimes?
Ravina spends a fair amount of time discussing how humanities researchers (he knows them better than I, certainly), insist on tables when they ask for data. I didn’t really read that into Adam’s criticism of these graphics—he was merely pointing out that data viz designers were making information too complex—he never claimed that the solution was to create charts. Then Ravina cites the misuse of pie charts to make the point that just because something is familiar, it can be misused. Is he implying that unfamiliar things can’t? As he puts it, “is schlock worse than shock?” Aside from the clever turn of phrase, it’s a bit of a moot point. Nothing that I have read criticizes innovation—merely obfuscation.
Mark Ravina: “Is schlock worse than shock?”
Ravina makes good points. He surveyed (presumably informally) graphs produced in history journals and notes that the bulk of them rely on formats developed (according to him) 200 years ago—pie charts, line charts and bar graphs. And he mentions how slow the field (I’m unclear if he means academics or history journals in particular) has been to adopt and thus understand formats that even today’s eighth graders are learning (box plots, for example). That’s a valid argument, certainly, but it has little to do with the complex visualizations that Adam was addressing or, for that matter, that Kosara and Few discuss. (To be fair, Ravina’s post was mostly in response to Adam’s).
However, he conflates different types of complexity, predictably citing Tufte and Menard (some of you know how I feel about that) as well as Rosling. Perhaps it’s a matter of taste, but I feel that Rosling bends over backwards to make his visualizations inspiring and accessible (not necessarily complex and beautiful), whereas the Menard graphic, while certainly elegant and ground-breaking, does not (of course not, and how could it, given when it was produced).
Lastly, one of the most important concerns that Adam raised was around obscuring data. By introducing unnecessary complexity into a visualization or graphic, data visualization designers can make academic and peer review verification and transparency needlessly difficult. Ravina counters this by saying that liars will lie. I don’t think that’s the point. They will lie, but transparency is as much about spotting errors or raising valid concerns as it is about unmasking willful deceit. Hats off to Ravina for taking the time to provide some very thoughtful counterpoints to the discussion.
Excelcharts is a pretty good resource for charting and data viz in general, despite the Excelcharts.com name (*smiling*). Jorge Camoes nicely (and literally) draws the elusive line between art/entertainment and data/information.
More importantly, he puts a restraining hand on eager designers, quite reasonably underscoring Few’s point to make sure that, as designers, we emphasize that charts and graphics are readable and easy to understand, not memorable or beautiful. Of course, I’ll see your readability and raise you ten, Jorge. Let’s make the data understandable and, if we can, beautiful as well.
Lastly, there is this. It is a tome. You could spend hours here. It’s an open-review paper, part of which is around data viz, part of which I have skimmed. It deserves careful reading, and I’m eager to do so and write a follow-up post.
Well, if you’ve hung in there with me, I hope you have learned something. I know I have.
There are not many good examples of concentric circle graphics out there. La Nacion produced one last year about subway strikes, and The Guardian produced an interactive graphic on gay rights in the U.S. Both of these intrigued me because, in my day job, I produce endless variations of graphics dealing with 50-state data. And most of the time, when we look at 50-state data, we draw… you guessed it: maps. Or bar graphs showing quantity or line graphs showing changes and trends over time but no matter what we do, it involves data for the 50 states, most often over time. 50 states multiplied by several years is a lot of lines to draw, bars to fill and state maps to create. So I’ve been thinking about ways to tell the story in different formats–going beyond the map, so to speak. Last Wednesday, we created this concentric circle interactive. Here’s how we did it, and the process we took to decide on the format.
One of the most onerous dimensions to 50-state data is the sheer physical size and length of the data. Our website used to allow for a content well of 500 pixels. Try shoving 50 state labels across 500 pixels and you’ll quickly see why it’s a challenge.
But even with all the real estate in the world, long, horizontal displays are also taxing on the user if there is a comparative aspect to the data. There is simply too much bouncing back and forth from the left to the right. Go long and you lose the comparative advantages of a horizontal layout because users with small screens must scroll vertically and can’t see the entire landscape at once. Of course, layering the data into different views as an interactive can solve that. But sometimes you want to show the data all at once. And for that, a static graphic can work well.
Understandably, a map is often the solution. But maps have their limitations too. There’s only so much that you can infer from a map. If your data consist of more than 4-5 gradations it can be tough to create the at-a-glance, concise overview for which a map is best suited.
And if there are no regional patterns discernible in your map, readers wind up staring at a jumble of color with only a legend to tie it all together.
Which brings me to concentric charts. They’re not pie charts (if you look up pie charts on wikipedia, you will see that there is a distant cousin to the pie chart called a “ring chart,” also known as a multi-level pie or a radial tree). These appear to be somewhat visually similar to concentric circle graphs but have a different use–they tend to show hierarchy in data–you might see these when your computer shows you how much disc space you have, for example.
A concentric chart, on the other hand, can tell a different story altogether. In a recent post on La Nacion’s subway strike graphic, I mentioned how designer Florencia Abd manages to plot out a time across four nodes (year, month, day and time) as well as another variable–type of incident/strike. That’s a lot of ground to cover in a static graphic. Imagine doing it in other ways and I’m sure you’ll agree.
Because a circle is, well, round, its shape lends itself quite well to a relationship-based approach. Not so much a pie-chart (where the user sees the parts in their physical relationship to the whole), but rather using the organic form of a circle to help the user more easily compare complex data. And if you add concentric circles, you take advantage of the hierarchy inherent to those circles to create layers–an intuitive way to order your data–perfect for showing levels or ratings where you use the inner and outer rings to denote the endpoints in a scale (e.g., one thing is stronger, larger or more intense on the outside than it is on the inside) or time, as the subway graphic above shows (the outer ring shows 5 a.m. and the inner ring shows 11 p.m.).
So, what does all this have to do with the U.S. map? As I mentioned, the strength of a map is to show geographic relationships in data. For example, southern states vote “red” (or conservative) in the U.S.; whereas a swath northeastern states might vote “blue” (progressive). For this, a map is helpful because regional differences tell the story and are easy to spot.
But the nice thing about concentric charts is that they, too, can show geography, or any groupings, for that matter. As the Guardian’s example shows, each “slice” of the concentric chart belongs to a state and groups of slices are regions. In the Guardian example, each ring (or level) of the chart denotes a particular right afforded to gay couples.
My team took this in a different direction. We wanted to show states and regions as well. But we also wanted to show change over time, as well as intensity on a scale. So when the Bureau of Labor Statistics released its employment figures, we had a few choices. We needed to show how changes in employment have affected each state since the recession (from April, 2007 to April, 2012). Because the recession started in December, 2007, we wanted to show how employment looked in each state before the recession, during the recession and how (and which) states were pulling themselves out of the recession.
We could have created an interactive that showed how the same views above changed over time (presumably you’d see a pre-recession view showing states doing well, a recession view showing most states doing poorly, and post-recession years showing mixed results). The most valuable piece of this would be, of course, geographical patterns in the data, if they existed (how did the Rust Belt fare, or the East Coast, for example). You could overlay this with population or any other demographic data to tell an interesting story.
When we looked at the data, we saw that there were not very strong geographic patterns to show. So we decided to create a concentric chart. Why? Because we didn’t have geographic patterns, but we did have temporal patterns (most states did poorly during a particular period of time, which contrasted well with the mixed results that states showed as they were attempting to pull themselves out of the recession, at least in terms of their employment figures). And the fact that we used a circle meant that we didn’t have to create a very long or wide table or chart, and we could stray from the map approach.
We decided to make this a light interactive–by rolling your cursor over each state’s cell you can see a small bar graph showing change in employment over time. This worked for us because our goal wasn’t to show specific numbers (how much employment rose and fell in a particular state), but rather intensity and patterns over time.
The debate continues (check out the comments on Nathan Yau’s post on the Guardian graphic) on whether or not these concentric graphs are merely eye candy when a simple bar or line chart would do just as well. I would opine that, if used correctly, they work well. Let me know if you agree. Here’s a screenshot of our interactive, and you can view the live version here.
I’m beginning to realize that, for developing countries like Bolivia, technology (by that I mean information and communications technologies ranging from cellphones and internet access, usage and affordability to the use of social media) is a chicken-and-egg dynamic. For Bolivia, both the egg and the chicken seem out of reach, though there are signs that some things might be improving.
The World Economic Forum and INSEAD recently released the 2012 Global Information Technology Report which scores 142 world economies on their use of information and communications technologies. Below is an infographic that I designed detailing how poorly and how well (mostly the former) Bolivia is using technology to improve the lives of its citizens and to become modestly globally competitive in, as the report puts it, “a hyper connected world.”
Don’t get too depressed, there are some bright spots. If you’re interested, read more about how a newspaper in Argentina is using open data to circumvent its government’s lack of open data transparency. And if you’re really interested, e-mail me.
The good (rankings out of 12 countries in South America):
- Bolivia’s political and regulatory environment (as it relates to technology) ranks 7th in South America.
- Although Bolivia ranks last in business and innovation, it does show a relatively high (3rd) availability of venture capital.
- Overall, the quality of Bolivia’s math and science education, its educational system overall, and its adult literacy rate all rank 7, 7 and 8, respectively.
- And, though Bolivia’s individual usage of technologies ranked last (12th), its citizen participation measure ranks a promising 6.
- Additionally, Bolivia’s capacity for innovation rank (5) is highly encouraging, despite another last place ranking for business usage of information and communications technologies overall.
- One of the most clear challenges for Bolivia is to increase the affordability, availability and reliability of its Information and Communication Technologies (ICT) to its citizens and the businesses that operate within its borders.
- Bolivia ranks last, or close to last, along almost every index. The country’s overall Network Readiness rank is 12.
Three solid entries from Spain, Brazil and Argentina are among the 58 nominees featured in the first-ever international competition for data journalism, the Data Journalism awards. The awards, announced by the Global Editors Network, will be announced on May 31. In the meantime, keep your eye on these three nominees:
“La trama de la SGAE,” from El Mundo’s Spanish designer David Alameda, covers last year’s “Operation Saga,” an undercover investigation of fraudulent financial activities conducted by the president and other members of Spain’s influential Society of Authors and Editors (SGAE). This piece boils down the complex network of who gave money to whom, how much and when into one of the best examples of interactive flowcharts that I’ve seen. As with the best data visualizations, this interactive avoids the many common mis-steps that could have occurred through the overuse of photos, text, talking heads, etc. Instead, Alameda keeps his focus–and ours–on a tightly scripted interactive that guides the user quickly and efficiently through the web of financial whodunits.
2011 Brazil State-Level Business Environment Ranking ranks the country’s business environment along eight categories (ranging from the political climate to innovation) and a series of indicators specific to each category. The interface is clean and simple to understand. Navigation, categories and indicators are well-prioritized and intuitive. One of my favorite features is the linked rollover behaviour between all four elements on the screen: a regional map, a deeper state-specific map, a regional bar graph and an overall scoring graph. A lot of information packed into a clean, well-designed interactive.
Lastly, Argentina’s La Nación is doing great stuff with open data. By my calculations, given that the country ranks sixth of 12 South American countries (and 92nd out of 142 economies globally, according to the recent Global Information and Technology Report’s Networked Readiness Index), this is a telling example of how Argentina’s relatively advanced use of information and communication technologies seem to be paying off, even if its government doesn’t always play along.
La Nación’s Subsidies for the Bus Transportation System is not so much a data visualization as a series of efforts to use open data to report on how bus subsidies in Argentina are being conducted. Dig a little and you’ll find a few good infographics, investigative pieces that detail a government’s efforts to be less than transparent about dollar figures, and an encouraging collaboration between the newspaper and Junar’s open data platform to create a Tableau dashboard that is beginning to circumvent Argentina’s lack of open data infrastructure. Interestingly, the newspaper compares its early efforts to the U.S.’s Freedom of Information Act laws and the American government’s data.gov platform. The dashboard presents a snapshot of indicators key to Argentina (ranging from crime and accident rates to political indicators and legislative data). It’s a promising approach that may help other countries (like Bolivia) with similar challenges (see related article on Bolivia’s recent technology rankings).
It is by now a cliché to to point out how developing countries most in need of what data journalism provides–a credible, fact-based approach that cuts through the noise of bias to help average citizens become informed participants in the problem-solving processes of improving social-political challenges–is not (quite) manifesting itself where it is most needed. Yeah, that’s a long sentence. But Bolivia is a case in point.
A search for data visualization in Bolivia yields mostly European NGOs posting myriad Tableau and GoogleMap visualizations about the usual statistics on health and economy–laudable efforts in their own right, but not a good representation of the state of information and data visualization in Bolivia proper.
To find what Bolivians are doing, you need lots of time and a high level of tolerance for dead links. But it’s out there. As a recent example, Bolivian@s Globales produced a modern, candid video on the state of Bolivia. It’s a solid blend of information and optimism, and shows us what today’s Bolivians are capable of producing in the digital space.
And–in a country where where the government can be reliably counted upon to discourage openness and transparency–multimedia, even the simple use of video, is critical. Fortunately, there is evidence that digital journalism is growing. The major papers went online years ago, but more importantly, there are now digital journalism sites and signs that Bolivian bloggers are growing, both in quality and in numbers.
Crowdsourcing, mapping and social media in Bolivian elections
Sadly, one of the most encouraging examples of data visualization and social media in Bolivia went dark, but the screenshots and documentation that remain are encouraging. In 2009, Voces Bolivianas and other Bolivians began using data visualization to monitor Bolivian elections (Elecciones 2.0 Bolivia). See how monitoring was crowdsourced through GoogleMaps:
Coupled with Twitter, a Facebook page and other social media, Elecciones 2.0 Bolivia was groundbreaking for Bolivians. Re-visto, an online investigative journalism site run by Deutche Welle, interviewed Mario Duran (a noted Bolivian blogger) on the groundswell of acceptance and use of social media and digital journalism in the 2009 elections (English translation here). And there was a New York Times write-up of how Bolivians were covering the elections referendum on Twitter.
Other Bolivian data visualization projects of note:
- Ushahidi (a project that develops open-source tools for programmers) provided the platform for a CrowdMap tool that Bolivians are using to document civil unrest in Bolivia. Guatemalans are using the tool to map incidents of citizen extortion.
- There’s also FLOW: a Water for People interactive using GoogleMaps that shows the quality of safe and reliable water supplies in countries such as Bolivia.
Bolivians’ access to reliable Internet:
Bolivia (as well as other developing nations and rural communities in the U.S.) faces another challenge–reliable internet speeds. A recent Bolivian infographic (in Spanish) describes the problem and the social media citizen lobbying effort (Mas y major internet en Bolivia–Better and more Internet in Bolivia) to address it.
I’ll be honest. As I was researching information for this post, I found myself frustrated with the fact that, after days of searching, I couldn’t easily point to a few examples of cutting edge data visualization pieces. There was a part of me that wanted to say to the world, “see, we’re doing it too, you just haven’t found us.” But I’m walking away from this experience with a much more sober understanding of the challenges that Bolivians face. I’m not a journalist. I no longer live in Bolivia. I don’t have to deal with civil unrest, strikes, sketchy Internet access and the uneasy history that Bolivian governments have bequeathed to journalists and citizens concerned with civil liberties and human rights.
The willingness of Bolivians to put in the sweat equity to learn, exploit and disseminate these technologies is self-evident and encouraging.
The next steps, as I see them? Helping Bolivian journalists continue to embrace data journalism, raising awareness of open source data platforms such as Tableau and Ushahidi, and empowering today’s technology-minded Bolivians to learn how to turn information into power through openness and transparency. I’d be most interested in hearing from you on how this is happening and look forward to writing more about it.
I’ve been on a multimedia kick lately, digging for interesting examples of how journalists are telling their stories via this interesting catch-all for pictures, animations and all things that move with words. A multimedia interactive timeline produced back in September, 2010 persists, in my view, as a stellar example. Yes, that was over a year-and-a-half ago, but I challenge you to find anything this good that has come out since.
El Mundo, a Spanish newspaper with a very good data visualization design team, created an interactive data visualization/multi-media narrative recreating the attempts to rescue Chilean miners trapped in the copper-gold mine near Copiapó in August 5, 2010 “Rescate de los mineros chilenos atrapados bajo tierra” (“Rescue of Chilean Miners Trapped Underground”).
Created a month after the successful rescue this piece by David Almeda successfully deconstructs the messy reality of three rescue plans, changing information on the ground, technical obstacles and engineering solutions, as well as the human faces behind the crisis. If I counted correctly, there are about 30 animated frames in this, several of which contain infographics polished enough to be published in their own right. The only thing I’d add to this would be a scrubber with a timeline to allow users to move through this at their own pace and to get a sense of the timing.
This is a solid interactive and a beautifully understated display of process, timelines and information. In our ongoing fascination with data visualization, this reminds me of why I started this blog.